Online analytical processing

On Line Analytical Processing, oder OLAP (IPA: [oːlæp]), is an approach to quickly providing answers to analytical queries that are multidimensional in nature. OLAP is part of the broader category business intelligence, which also includes Extract transform load (ETL), relational reporting and data mining. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (On Line Transaction Processing).

Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. Nigel Pendse has suggested that an alternative and perhaps more descriptive term to describe the concept of OLAP is Fast Analysis of Shared Multidimensional Information (FASMI). They borrow aspects of navigational databases and hierarchical databases that are speedier than their relational kin.

The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the row and column of the matrix; the measures, the values.

Functionality

In the core of any OLAP system is a concept of an OLAP cube (also called a multidimensional cube or an hypercube). It consists of numeric facts called measures which are categorized by dimensions. The cube metadata is typically created from a star schema or snowflake schema of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. In MOLAP(Multidimensional OLAP) products the cube is populated by copying snapshot of the data from the data source, ROLAP(Relational OLAP) products work directly against the data source without copying data and HOLAP(Hybrid OLAP) products combine the previous two approaches.

Aggregations

It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time for the same query on OLTP relational data ^{[citation needed]}. The single most important mechanism in OLAP which allows to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. The number of possible aggregations is determined by every possible combination of dimension granularities.

The combination of all possible aggregations and the base data contain the answers to every query which can be answered from the data (as in Gray, Bosworth, Layman, and Pirahesh, 1997). Due to the potentially large number of aggregations to be calculated, often only a predetermined number are fully calculated while the remainder are solved on demand. The problem of deciding which aggregations (a.k.a. views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time as well. Many different approaches have been taken to view selection (which is NP-Complete), including greedy algorithms, randomized search, genetic algorithms and A* search algorithms.

Types

OLAP systems have been traditionally categorized using the following taxonomy

Multidimensional

MOLAP is the 'classic' form of OLAP and is sometimes referred to as just OLAP. MOLAP uses database structures that are generally optimal for attributes such as time period, location, product or account code. The way that each dimension will be aggregated is defined in advance by one or more hierarchies.

Relational

ROLAP works directly with relational databases. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. Depends on a specialized schema design.

Hybrid

There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage. For example, for some vendors, a HOLAP database will use relational tables to hold the larger quantities of detailed data, and use specialized storage for at least some aspects of the smaller quantities of more-aggregate or less-detailed data.

Comparison

Each type has certain benefits, although there is disagreement about the specifics of the benefits between providers.

Some MOLAP implementations are prone to database explosion. Database explosion is a phenomenon causing vast amounts of storage space to be used by MOLAP databases when certain common conditions are met: high number of dimensions, pre-calculated results and sparse multidimensional data. The typical mitigation technique for database explosion is not to materialize all the possible aggregation, but only the optimal subset of aggregations based on the desired performance vs. storage trade off.

MOLAP generally delivers better performance due to specialized indexing and storage optimizations. MOLAP also needs less storage space compared to ROLAP because the specialized storage typically includes compression techniques.

ROLAP is generally more scalable. However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer.

Since ROLAP relies more on the database to perform calculations, it has more limitations in the specialized functions it can use.

HOLAP encompasses a range of solutions that attempt to mix the best of ROLAP and MOLAP. It can generally pre-process quickly, scale well, and offer good function support.

Other types

The following acronyms are also used sometimes, although they are not as widespread as the ones above

WOLAP - Web-based OLAP
DOLAP - Desktop OLAP
RTOLAP - Real-Time OLAP
SOLAP - Spatial OLAP (see Location intelligence)

APIs and query languages

Unlike relational databases - which had SQL as the standard query language, and wide-spread APIs such as ODBC, JDBC and OLEDB - there was no such unification in the OLAP world for a long time. The first real standard API was OLE DB for OLAP (ODBO) specification from Microsoft which appeared in 1997 and introduced the MDX query language. Several OLAP vendors - both server and client - adopted it. In 2001 Microsoft and Hyperion announced the XML for Analysis specification, which was endorsed by most of the OLAP vendors. Since this also used MDX as a query language, MDX became the de-facto standard in the OLAP world.

Produkte

History

The first product that performed OLAP queries was IRI's (Information Resources Incorporated )Express, which was released in 1970 (and acquired by Oracle in 1995). However, the term did not appear until 1993 when it was coined by Ted Codd, who has been described as "the father of the relational database". Codd's paper resulted from a short consulting assignment which Codd undertook for former Arbor Software (now Hyperion Solutions), as a sort of marketing coup: the company had released its own OLAP product — Essbase — a year earlier. As a result Codd's "twelve laws of online analytical processing" were explicit in their reference to Essbase. There was some ensuing controversy and when Computerworld learned that Codd was paid by Arbor, it retracted the article. OLAP market experienced strong growth in late 90s with dozens of commercial products going into market. In 1998, Microsoft released its first OLAP Server - Microsoft Analysis Services, which drove wide adoption of OLAP technology and moved it into mainstream. In the mid 2000, the Open Source OLAP market began to establish itself, with several companies springing up with offerings.

Market shares

According to the influential OLAP Report site, the market shares for the top commercial OLAP products in 2006 were:

Microsoft Corporation - 31.6%
Hyperion Solutions Corporation - 18.9%
Cognos - 12.9%
Business Objects - 7.3%
MicroStrategy - 7.3%
SAP AG - 5.8%
Cartesis SA - 3.7%
Applix - 3.6%
Infor - 3.5%
Oracle Corporation - 3.4%

External links